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Showing 1 to 3 of 3 for “"Non-Parametrics"”.

  1. Non-Parametric Priors for Functional Data and Partition Labelling Models

    <p>Previous papers introduced a variety of extensions of the Dirichlet process to the func-</p><p>tional domain, focusing on the challenges presented by extending the stick-breaking</p><p>process. In this thesis some of these are examined in more detail for similarities</p><p>and differences in …

    duke Repository record for Non-Parametric Priors for Functional Data and Partition Labelling Models (opens in a new tab)

  2. Non-parametric modelling of signals on graphs

    … that Gaussian processes, a class of Bayesian non-parametric models, are particularly well suited for modelling data on graph domains. To provide evidence for this hypothesis, I demonstrate the merits of Bayesian non-parametric modelling for graph data by deriving Gaussian process models for …

    cambridge Repository record for Non-parametric modelling of signals on graphs (opens in a new tab)

  3. Hierarchical Inference in Gaussian Processes

    … of Gaussian process models in the context of non-parameteric regression and latent variable modelling. While in earlier chapters the focus remains largely on the traditional setting of hyperparameter inference, we also develop a novel hierarchical construction leveraging modern neural networks …

    cambridge Repository record for Hierarchical Inference in Gaussian Processes (opens in a new tab)